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Found 20 papers, 11 papers with code
From Barlow Twins to Triplet Training: Differentiating Dementia with Limited Data
To address these issues, we propose Triplet Training for differential diagnosis with limited target data.
Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning
During inference, similarities of latent features to prototypes are linearly classified to form predictions and attribution maps are provided to explain the similarity.
Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease
We propose PANIC, a prototypical additive neural network for interpretable AD classification that integrates 3D image and tabular data.
Joint Reconstruction and Parcellation of Cortical Surfaces
The reconstruction of cerebral cortex surfaces from brain MRI scans is instrumental for the analysis of brain morphology and the detection of cortical thinning in neurodegenerative diseases like Alzheimer's disease (AD).
CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis
To this end, we introduce CASHformer, a transformer-based framework to model longitudinal shape trajectories in AD.
Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs
AD classification and focus on differential diagnosis of dementia, where fusing multi-modal image information conforms with a clinical need.
Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networks
The reconstruction of cortical surfaces from brain magnetic resonance imaging (MRI) scans is essential for quantitative analyses of cortical thickness and sulcal morphology.
TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes
To the best of our knowledge, this is the first work that combines transformer and mesh networks.
Alzheimer's Disease Diagnosis via Deep Factorization Machine Models
The current state-of-the-art deep neural networks (DNNs) for Alzheimer's Disease diagnosis use different biomarker combinations to classify patients, but do not allow extracting knowledge about the interactions of biomarkers.
Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data
We propose Shapley Value Explanation of Heterogeneous Neural Networks (SVEHNN) for explaining the Alzheimer's diagnosis made by a DNN from the 3D point cloud of the neuroanatomy and tabular biomarkers.
Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform
Prior work on diagnosing Alzheimer's disease from magnetic resonance images of the brain established that convolutional neural networks (CNNs) can leverage the high-dimensional image information for classifying patients.
Semi-Structured Deep Piecewise Exponential Models
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning.
Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's Continuum
We derive a causal graph from the current clinical knowledge on cause and effect in the Alzheimer's disease continuum, and show that identifiability of the causal effect requires all confounders to be known and measured.
Detect and Correct Bias in Multi-Site Neuroimaging Datasets
Given such evidence, we take a closer look at confounding bias, which is often viewed as the main shortcoming in observational studies.
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data
Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers.
Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference
In this work, we combine 12, 207 MRI scans from 15 studies and show that simple pooling is often ill-advised due to introducing various types of biases in the training data.
Adversarial Learned Molecular Graph Inference and Generation
Recent methods for generating novel molecules use graph representations of molecules and employ various forms of graph convolutional neural networks for inference.
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
A disadvantage of FL is the dependence on a central server, which requires all clients to agree on one trusted central body, and whose failure would disrupt the training process of all clients.
'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images
This representation is passed on to the segmenter arm that uses this information to segment the new query image.
An Efficient Training Algorithm for Kernel Survival Support Vector Machines
Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support.
